Data & ML interviews. 6 titles, each with its own model.
Each page shows the competency model, the questions that test it, what changes with seniority, and where candidates lose the interview.
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Every Data & ML title
6 titlesWhat the report looks like for Data & ML
- The situation in one sentence: where, what was short, and by how much.
- The decision that was yours rather than the team’s, and what you chose not to do.
- One number or one consequence that shows it worked.
Every competency is scored out of five with a line you actually said as evidence, and the weakest answers are rewritten as structure and specifics, never a script.
What Data & ML interviewers keep scoring
Problem framing & success metrics
Translates a business or product goal into a well-posed ML task with a metric that reflects real value, and knows when ML is not the right tool.
Data quality & feature engineering
Understands and validates the data before modelling, engineers features with awareness of leakage and drift, and reproduces data pipelines reliably.
Modelling & rigorous evaluation
Selects and trains appropriate models, evaluates them honestly with proper baselines and error analysis, and can explain why a model made a prediction.
Deploying & operating ML in production
Ships models as reliable services or batch jobs with monitoring for drift, latency and quality, and can retrain, roll back and explain incidents.
LLM & generative AI systems
Builds applications on foundation models using prompting, retrieval, fine-tuning and evaluation, with attention to cost, latency, hallucination and safety.
Experimentation & causal thinking
Validates that a model actually improves outcomes through A/B tests or other causal methods, and avoids fooling themselves with offline gains.
Communicating results & uncertainty
Explains model behaviour, limitations and confidence to non-specialists so they make good decisions, and is honest about what the model cannot do.
Responsible AI, fairness & data privacy
Assesses bias, fairness and privacy risks in data and models, and applies appropriate safeguards and regulatory awareness (e.g. POPIA/GDPR consent and purpose limitation).